Optimizing Ontology Alignment through Improved NSGA-II
Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker (DM) without any user preferences. This work...
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| Format: | Article |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/8586058 |
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| author | Yikun Huang Xingsi Xue Chao Jiang |
| author_facet | Yikun Huang Xingsi Xue Chao Jiang |
| author_sort | Yikun Huang |
| collection | DOAJ |
| description | Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker (DM) without any user preferences. This work investigates the ontology matching problem, which is a challenge in the semantic web (SW) domain. Due to the complex heterogeneity between two different ontologies, it is arduous to get an excellent alignment that meets all DMs’ demands. To this end, a popular MOEA, i.e., nondominated sorting genetic algorithm (NSGA-II), is investigated to address the ontology matching problem, which outputs the knee solutions in the PF to meet diverse DMs’ requirements. In this study, for further enhancing the performance of NSGA-II, we propose to incorporate into NSGA-II’s evolutionary process the monkey king evolution algorithm (MKE) as the local search algorithm. The improved NSGA-II (iNSGA-II) is able to better converge to the real Pareto optimum region and ameliorate the quality of the solution. The experiment uses the famous benchmark given by the ontology alignment evaluation initiative (OAEI) to assess the performance of iNSGA-II, and the experiment results present that iNSGA-II is able to seek out preferable alignments than OAEI’s participators and NSGA-II-based ontology matching technique. |
| format | Article |
| id | doaj-art-b42bfb8fcf6e454cba086e9b51f2df38 |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-b42bfb8fcf6e454cba086e9b51f2df382025-08-20T02:23:12ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/85860588586058Optimizing Ontology Alignment through Improved NSGA-IIYikun Huang0Xingsi Xue1Chao Jiang2Concord University College, Fujian Normal University, Fuzhou 350118, ChinaCollege of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian 350118, ChinaCollege of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian 350118, ChinaOver the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker (DM) without any user preferences. This work investigates the ontology matching problem, which is a challenge in the semantic web (SW) domain. Due to the complex heterogeneity between two different ontologies, it is arduous to get an excellent alignment that meets all DMs’ demands. To this end, a popular MOEA, i.e., nondominated sorting genetic algorithm (NSGA-II), is investigated to address the ontology matching problem, which outputs the knee solutions in the PF to meet diverse DMs’ requirements. In this study, for further enhancing the performance of NSGA-II, we propose to incorporate into NSGA-II’s evolutionary process the monkey king evolution algorithm (MKE) as the local search algorithm. The improved NSGA-II (iNSGA-II) is able to better converge to the real Pareto optimum region and ameliorate the quality of the solution. The experiment uses the famous benchmark given by the ontology alignment evaluation initiative (OAEI) to assess the performance of iNSGA-II, and the experiment results present that iNSGA-II is able to seek out preferable alignments than OAEI’s participators and NSGA-II-based ontology matching technique.http://dx.doi.org/10.1155/2020/8586058 |
| spellingShingle | Yikun Huang Xingsi Xue Chao Jiang Optimizing Ontology Alignment through Improved NSGA-II Discrete Dynamics in Nature and Society |
| title | Optimizing Ontology Alignment through Improved NSGA-II |
| title_full | Optimizing Ontology Alignment through Improved NSGA-II |
| title_fullStr | Optimizing Ontology Alignment through Improved NSGA-II |
| title_full_unstemmed | Optimizing Ontology Alignment through Improved NSGA-II |
| title_short | Optimizing Ontology Alignment through Improved NSGA-II |
| title_sort | optimizing ontology alignment through improved nsga ii |
| url | http://dx.doi.org/10.1155/2020/8586058 |
| work_keys_str_mv | AT yikunhuang optimizingontologyalignmentthroughimprovednsgaii AT xingsixue optimizingontologyalignmentthroughimprovednsgaii AT chaojiang optimizingontologyalignmentthroughimprovednsgaii |